@Article{jcs.2021.017082,
AUTHOR = {Yunzuo Zhang, Kaina Guo, Wei Guo, Jiayu Zhang, Yi Li},
TITLE = {Pedestrian Crossing Detection Based on HOG and SVM},
JOURNAL = {Journal of Cyber Security},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {79--88},
URL = {http://www.techscience.com/JCS/v3n2/43996},
ISSN = {2579-0064},
ABSTRACT = {In recent years, pedestrian detection is a hot research topic in the field 
of computer vision and artificial intelligence, it is widely used in the field of 
security and pedestrian analysis. However, due to a large amount of calculation 
in the traditional pedestrian detection technology, the speed of many systems for 
pedestrian recognition is very limited. But in some restricted areas, such as 
construction hazardous areas, real-time detection of pedestrians and cross-border 
behaviors is required. To more conveniently and efficiently detect whether there 
are pedestrians in the restricted area and cross-border behavior, this paper 
proposes a pedestrian cross-border detection method based on HOG (Histogram 
of Oriented Gradient) and SVM (Support Vector Machine). This method extracts 
the moving target through the GMM (Gaussian Mixture Model) background 
modeling and then extracts the characteristics of the moving target through 
gradient HOG. Finally, it uses SVM training to distinguish pedestrians from nonpedestrians, completes the detection of pedestrians, and labels the targets. The 
test results show that only the HOG feature extraction of the candidate area can 
greatly reduce the amount of calculation and reduce the time of feature extraction, 
eliminate background interference, thereby improving the efficiency of detection, 
and can be applied to occasions with real-time requirements.},
DOI = {10.32604/jcs.2021.017082}
}